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- W2797918600 endingPage "80" @default.
- W2797918600 startingPage "57" @default.
- W2797918600 abstract "Temporally, precise correlations between simultaneously recorded neurons have been interpreted as signatures of cell assemblies, i.e., groups of neurons that form processing units. Evidence for this hypothesis was found on the level of pairwise correlations in simultaneous recordings of few neurons. Increasing the number of simultaneously recorded neurons increases the chances to detect cell assembly activity due to the larger sample size. Recent technological advances have enabled the recording of 100 or more neurons in parallel. However, these massively parallel spike train data require novel statistical tools to be analyzed for correlations, because they raise considerable combinatorial and multiple testing issues. Recently, various of such methods have started to develop. First approaches were based on population or pairwise measures of synchronization, and later led to methods for the detection of various types of higher-order synchronization and of spatio-temporal patterns. The latest techniques combine data mining with analysis of statistical significance. Here, we give a comparative overview of these methods, of their assumptions and of the types of correlations they can detect." @default.
- W2797918600 created "2018-04-24" @default.
- W2797918600 creator A5048613858 @default.
- W2797918600 creator A5072220479 @default.
- W2797918600 creator A5078169632 @default.
- W2797918600 creator A5078775405 @default.
- W2797918600 date "2018-04-01" @default.
- W2797918600 modified "2023-10-18" @default.
- W2797918600 title "Methods for identification of spike patterns in massively parallel spike trains" @default.
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